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What is SAS Solution ?
It is a software from SAS Institute which consists of four
independent but integrated component
1. SAS Enterprise Miner
2. SAS/Warehouse AdministratorTM Software
3. SAS Solution for OLAP
4. SAS/IntrNetTM
SAS Enterprise Manager
Is a software that provide
1. Advanced, easy to use statistical analysis
2. A guided flexible, SEMMA methodology
3. Client/ Server enablement
4. Easy to use Graphical user interface (GUI)
The SEMMA Methodology
S
Sample Node
E
Explore Node
M
Modify Node
M
Model Node
A
Assess Node
Sampling Node
Use Sample data can reduce the
amount of process time
But be Sure of
The sample data are sufficiently representative of
the whole, patterns that appears in the entire
databases.
Sampling Methods Used by SAS
1. Random (Default)
2. Every n observation
3. Stratified observation
4. First n observation
5.Cluster
Exploring Node
SAS Miner is supported with numerous
90
tools to explore data like
80
70
1. Graphical Display
60
50
2. Outlier Filter
E as t
Wes t
40
N orth
30
3. Transformation
20
10
0
1s t Q tr
2nd Q tr
3rd Q tr
4th Q tr
Exploring Cont…..

Distribution Explorer Node

Multiplot Node

Insight Node

Associations Node

Variable Selection Node
Modifying Node
In the distribution Node, there are
two possible modifying methods
Modify Data Set attributes
Modify meta data sample
Modifying Cont....

Transform Variable Node
Transform Variables to improve the fit of the model to
the data

Filter outlier Node
1. Eliminate rare values
2. Eliminate missing values
3. Replace missing values with (Mean)
Modeling Node
Now is the time to choose the best Model
It uses
1. optimization methods
(Gradient, Newton,Quasi Newton,……)
2. Statistical Significant tests
(Chi Square, F. Test,…….)
Assessment Node
Provide a common framework to compare models and
predictions from any Analytical tool in Enterprise
Miner
Cross Model Comparison
Main Criteria. Actual profits
Conclusion
1. SAS enterprise Miner fully integrates all
2. It has
intuitive
flexible
GUI,beginning
which enables
steps
of data
mining
process
with
users,
who may
different
degrees of
the
sampling
of have
the data,
through
statistical experience,
to plan,and
implement,
and
sophisticated
data analysis
modeling,
to
refine
their data mining
the
dissemination
of theprojects.
resulting
information